In the field of aviation, notably civil aviation, most aircraft are provided with meteorological radars or receive meteorological information. Meteorological information can originate from radars on board other aircraft, from meteorological radars on the ground for example. The availability, to an aircraft crew, of real-time meteorological data is crucial in the course of a flight so as to be able to avoid meteorological situations that could endanger the aircraft as well as its passengers. For example, aircraft pilots avoid entering convective cells like cumulonimbus. To this end, a meteorological situation representing notably clouds in a horizontal or vertical plane can allow a crew to avoid such zones of disturbances. Moreover, the ability to view a prediction of a short-term trend of the disturbance zones, may allow a crew to find the best possible trajectory for passing through an unstable meteorological zone.
Numerous schemes for modeling a meteorological situation have been developed, so as notably to be able to perform predictions of the trend of the situation. Objects making up a meteorological situation may be multifold: wind field, temperature field, clouds, precipitations. Objects such as clouds and associated precipitations can for example be described by using geometric information, texture information. Clouds and precipitations can also be described by a temporal trend of certain characteristics such as their size, the temperature. Among the existing modelings, a first modeling of clouds is described by Arnaud Y., Desbois M., Maizi J., in the following publication: “Automatic tracking and characterization of African convective systems on Meteosat pictures, Journal of Applied Meteorology, vol. 31, No. 5, 1992”. This first modeling uses notably measurements of areas, of center of gravity, of extension length along two axes of each cloud. The form of each cloud is extracted beforehand from an image provided by a satellite radar. The extraction is carried out by a thresholding scheme.
A second modeling is proposed by Yang Y., Lin H., Guo Z., Fang Z., Jiang J., in the following publication: “Automatic tracking and characterization of multiple moving clouds in satellite images, IEEE international conference on systems, man and cybernetics, vol. 4, 2004”. The scheme proposed by the second modeling is based notably on a contour of each cloud so as to measure an area, a deformation, a stretching of the cloud. The second modeling also uses Fourier descriptors for the contour of the cloud and a texture measurement performed on an image arising from the meteorological radar.
A third modeling is described by Dell'Acqua F., Gamba P., in the following publication: “A simple modal approach to the problem of meteorological object tracking, Geoscience and Remote Sensing Symposium, vol. 5, 2000”. The third modeling proposes a representation of the contours of the clouds by eigenvectors, together with detection of the centroids of each cloud.
A fourth modeling is cited by Papin C., in “Analyse spatia-temporelle d'images météorologiques satellitaires: détection et suivi de structures nuageuses critiques” [Spatio-temporal analysis of satellite meteorological images: detection and tracking of critical cloud structures], Thesis of the University of Rennes 1, heading Signal processing and telecommunication, 1999”. The fourth modeling uses identification of convective clouds by their contours, said contours being detected by a so-called level lines or active contours scheme.
A fifth modeling is described by Barbaresco F., Monnier B. in the following publication: “Rain clouds tracking with radar image processing based on morphological skeleton matching, International conference on image processing, vol. 1, 2001”. In the fifth modeling, clouds are represented by their morphological skeleton.
Certain modeling criteria allow correct interpretation by a human being, this being the case notably for modeling schemes using centroids, contours and geometric criteria like areas, extensions of the forms. However, when a scheme takes the contour of the objects into account, the resulting modeling lacks robustness if the initial image is noisy. Another reason for this lack of robustness is also the fact that clouds are fluid objects whose contours can fluctuate without this being pertinent for the trend of the meteorological phenomenon. Modelings using contours are therefore not optimal for use by a computer-based processing like tracking. The modeling schemes using contours are notably the first, second and fourth modelings, as well as the third modeling.
Other modeling criteria give a modeling that is unintelligible to a human being since they are aimed only at providing a description of each cloud, adapted for use by computer-based processing. Such is notably the case for the second modeling scheme using Fourier descriptors and textures as well as for the third modeling scheme using a representation of the contours by eigenvectors. Indeed, eigenvector representation does not allow direct interpretation of the situation by a human operator.
The fifth modeling scheme gives a robust representation of a set of convective cells. However, with the fifth modeling scheme, there are losses of the gray level information, and therefore of the exact location of the convective cells. It is therefore not possible to follow the life cycle, for example their expansion, the maturity, the decay of the convective cells.
Moreover, the schemes described above exhibit the defect of fixing a spatial scale for analyzing the meteorological data. For example certain schemes use a low thresholding of the gray level images, for example of the order of some twenty or so dBZ, dBZ being an abbreviation for decibel Z. A low thresholding only allows tracking of big convective systems, thus it is impossible with such schemes to perform a prediction of the development of convective systems. Other schemes use a thresholding for example of the order of forty dBZ. Such a thresholding makes it possible only to locate the most intense convection zones: the individual cells of the convective systems. The identification of a convective cell from one image to another is not very robust, notably in the case of convective systems with several close cells. These schemes are therefore not very robust for performing trackings of the convective cells and deducing therefrom a prediction of their trend.